from scipy.stats import pearsonr

import datasets


__a    :	Union[str, Any]         =    """
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.
The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.
"""


__a    :	Optional[Any]         =    """
Args:
    predictions (`list` of `int`): Predicted class labels, as returned by a model.
    references (`list` of `int`): Ground truth labels.
    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.

Returns:
    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.
    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.

Examples:

    Example 1-A simple example using only predictions and references.
        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])
        >>> print(round(results['pearsonr'], 2))
        -0.74

    Example 2-The same as Example 1, but that also returns the `p-value`.
        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)
        >>> print(sorted(list(results.keys())))
        ['p-value', 'pearsonr']
        >>> print(round(results['pearsonr'], 2))
        -0.74
        >>> print(round(results['p-value'], 2))
        0.15
"""


__a    :	int         =    """
@article{2020SciPy-NMeth,
author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
      Haberland, Matt and Reddy, Tyler and Cournapeau, David and
      Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
      Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
      Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
      Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
      Kern, Robert and Larson, Eric and Carey, C J and
      Polat, Ilhan and Feng, Yu and Moore, Eric W. and
      {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
      Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
      Harris, Charles R. and Archibald, Anne M. and
      Ribeiro, Antonio H. and Pedregosa, Fabian and
      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
      Computing in Python}},
journal = {Nature Methods},
year    = {2020},
volume  = {17},
pages   = {261--272},
adsurl  = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
"""




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,_KWARGS_DESCRIPTION					)
class   _UpperCamelCase (		datasets.Metric					):


			"""simple docstring"""






			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Union[str, Any]:

							'''simple docstring'''


							return datasets.MetricInfo(
							    description=_DESCRIPTION ,  citation=_CITATION ,  inputs_description=_KWARGS_DESCRIPTION ,  features=datasets.Features(
							        {
							            '''predictions''': datasets.Value('''float'''		),
							            '''references''': datasets.Value('''float'''		),
							        }		) ,  reference_urls=['''https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html'''] ,  )





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__=False		)	->   Union[str, Any]:

							'''simple docstring'''


							if return_pvalue:
											__lowercase					     =				pearsonr(lowerCAmelCase__ ,  lowerCAmelCase__		)
											return {"pearsonr": results[0], "p-value": results[1]}
							else:
											return {"pearsonr": float(pearsonr(lowerCAmelCase__ ,  lowerCAmelCase__		)[0]		)}
import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
__a    :	Tuple         =    logging.get_logger(__name__)





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				__lowercase					     =				SwinConfig.from_pretrained(
				    '''microsoft/swin-tiny-patch4-window7-224'''				,     out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']				)
				__lowercase					     =				MaskFormerConfig(backbone_config=lowercase				)

				__lowercase					     =				'''huggingface/label-files'''
				if "ade20k-full" in model_name:
								# this should be ok
								__lowercase					     =				847
								__lowercase					     =				'''maskformer-ade20k-full-id2label.json'''
				elif "ade" in model_name:
								# this should be ok
								__lowercase					     =				150
								__lowercase					     =				'''ade20k-id2label.json'''
				elif "coco-stuff" in model_name:
								# this should be ok
								__lowercase					     =				171
								__lowercase					     =				'''maskformer-coco-stuff-id2label.json'''
				elif "coco" in model_name:
								# TODO
								__lowercase					     =				133
								__lowercase					     =				'''coco-panoptic-id2label.json'''
				elif "cityscapes" in model_name:
								# this should be ok
								__lowercase					     =				19
								__lowercase					     =				'''cityscapes-id2label.json'''
				elif "vistas" in model_name:
								# this should be ok
								__lowercase					     =				65
								__lowercase					     =				'''mapillary-vistas-id2label.json'''

				__lowercase					     =				json.load(open(hf_hub_download(lowercase				,     lowercase				,     repo_type='''dataset'''				)				,     '''r'''				)				)
				__lowercase					     =				{int(lowercase				): v for k, v in idalabel.items()}

				return config





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				__lowercase					     =				[]
				# stem
				# fmt: off
				rename_keys.append(('''backbone.patch_embed.proj.weight''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight''')				)
				rename_keys.append(('''backbone.patch_embed.proj.bias''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias''')				)
				rename_keys.append(('''backbone.patch_embed.norm.weight''', '''model.pixel_level_module.encoder.model.embeddings.norm.weight''')				)
				rename_keys.append(('''backbone.patch_embed.norm.bias''', '''model.pixel_level_module.encoder.model.embeddings.norm.bias''')				)
				# stages
				for i in range(len(config.backbone_config.depths				)				):
								for j in range(config.backbone_config.depths[i]				):
												rename_keys.append((F"backbone.layers.{i}.blocks.{j}.norm1.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight")				)
												rename_keys.append((F"backbone.layers.{i}.blocks.{j}.norm1.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias")				)
												rename_keys.append((F"backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table")				)
												rename_keys.append((F"backbone.layers.{i}.blocks.{j}.attn.relative_position_index", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index")				)
												rename_keys.append((F"backbone.layers.{i}.blocks.{j}.attn.proj.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight")				)
												rename_keys.append((F"backbone.layers.{i}.blocks.{j}.attn.proj.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias")				)
												rename_keys.append((F"backbone.layers.{i}.blocks.{j}.norm2.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight")				)
												rename_keys.append((F"backbone.layers.{i}.blocks.{j}.norm2.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias")				)
												rename_keys.append((F"backbone.layers.{i}.blocks.{j}.mlp.fc1.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight")				)
												rename_keys.append((F"backbone.layers.{i}.blocks.{j}.mlp.fc1.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias")				)
												rename_keys.append((F"backbone.layers.{i}.blocks.{j}.mlp.fc2.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight")				)
												rename_keys.append((F"backbone.layers.{i}.blocks.{j}.mlp.fc2.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias")				)

								if i < 3:
												rename_keys.append((F"backbone.layers.{i}.downsample.reduction.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight")				)
												rename_keys.append((F"backbone.layers.{i}.downsample.norm.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight")				)
												rename_keys.append((F"backbone.layers.{i}.downsample.norm.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias")				)
								rename_keys.append((F"backbone.norm{i}.weight", F"model.pixel_level_module.encoder.hidden_states_norms.{i}.weight")				)
								rename_keys.append((F"backbone.norm{i}.bias", F"model.pixel_level_module.encoder.hidden_states_norms.{i}.bias")				)

				# FPN
				rename_keys.append(('''sem_seg_head.layer_4.weight''', '''model.pixel_level_module.decoder.fpn.stem.0.weight''')				)
				rename_keys.append(('''sem_seg_head.layer_4.norm.weight''', '''model.pixel_level_module.decoder.fpn.stem.1.weight''')				)
				rename_keys.append(('''sem_seg_head.layer_4.norm.bias''', '''model.pixel_level_module.decoder.fpn.stem.1.bias''')				)
				for source_index, target_index in zip(range(3				,     0				,     -1				)				,     range(0				,     3				)				):
								rename_keys.append((F"sem_seg_head.adapter_{source_index}.weight", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight")				)
								rename_keys.append((F"sem_seg_head.adapter_{source_index}.norm.weight", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight")				)
								rename_keys.append((F"sem_seg_head.adapter_{source_index}.norm.bias", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias")				)
								rename_keys.append((F"sem_seg_head.layer_{source_index}.weight", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight")				)
								rename_keys.append((F"sem_seg_head.layer_{source_index}.norm.weight", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight")				)
								rename_keys.append((F"sem_seg_head.layer_{source_index}.norm.bias", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias")				)
				rename_keys.append(('''sem_seg_head.mask_features.weight''', '''model.pixel_level_module.decoder.mask_projection.weight''')				)
				rename_keys.append(('''sem_seg_head.mask_features.bias''', '''model.pixel_level_module.decoder.mask_projection.bias''')				)

				# Transformer decoder
				for idx in range(config.decoder_config.decoder_layers				):
								# self-attention out projection
								rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight", F"model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight")				)
								rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias", F"model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias")				)
								# cross-attention out projection
								rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight", F"model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight")				)
								rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias", F"model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias")				)
								# MLP 1
								rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight", F"model.transformer_module.decoder.layers.{idx}.fc1.weight")				)
								rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias", F"model.transformer_module.decoder.layers.{idx}.fc1.bias")				)
								# MLP 2
								rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight", F"model.transformer_module.decoder.layers.{idx}.fc2.weight")				)
								rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias", F"model.transformer_module.decoder.layers.{idx}.fc2.bias")				)
								# layernorm 1 (self-attention layernorm)
								rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight", F"model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight")				)
								rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias", F"model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias")				)
								# layernorm 2 (cross-attention layernorm)
								rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight", F"model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight")				)
								rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias", F"model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias")				)
								# layernorm 3 (final layernorm)
								rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight", F"model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight")				)
								rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias", F"model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias")				)

				rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.weight''', '''model.transformer_module.decoder.layernorm.weight''')				)
				rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.bias''', '''model.transformer_module.decoder.layernorm.bias''')				)

				# heads on top
				rename_keys.append(('''sem_seg_head.predictor.query_embed.weight''', '''model.transformer_module.queries_embedder.weight''')				)

				rename_keys.append(('''sem_seg_head.predictor.input_proj.weight''', '''model.transformer_module.input_projection.weight''')				)
				rename_keys.append(('''sem_seg_head.predictor.input_proj.bias''', '''model.transformer_module.input_projection.bias''')				)

				rename_keys.append(('''sem_seg_head.predictor.class_embed.weight''', '''class_predictor.weight''')				)
				rename_keys.append(('''sem_seg_head.predictor.class_embed.bias''', '''class_predictor.bias''')				)

				for i in range(3				):
								rename_keys.append((F"sem_seg_head.predictor.mask_embed.layers.{i}.weight", F"mask_embedder.{i}.0.weight")				)
								rename_keys.append((F"sem_seg_head.predictor.mask_embed.layers.{i}.bias", F"mask_embedder.{i}.0.bias")				)
				# fmt: on

				return rename_keys





def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				dct.pop(lowercase				)
				__lowercase					     =				val





def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				[int(backbone_config.embed_dim * 2**i				) for i in range(len(backbone_config.depths				)				)]
				for i in range(len(backbone_config.depths				)				):
								__lowercase					     =				num_features[i]
								for j in range(backbone_config.depths[i]				):
												# fmt: off
												# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
												__lowercase					     =				state_dict.pop(F"backbone.layers.{i}.blocks.{j}.attn.qkv.weight"				)
												__lowercase					     =				state_dict.pop(F"backbone.layers.{i}.blocks.{j}.attn.qkv.bias"				)
												# next, add query, keys and values (in that order) to the state dict
												__lowercase					     =				in_proj_weight[:dim, :]
												__lowercase					     =				in_proj_bias[: dim]
												__lowercase					     =				in_proj_weight[
												    dim : dim * 2, :
												]
												__lowercase					     =				in_proj_bias[
												    dim : dim * 2
												]
												__lowercase					     =				in_proj_weight[
												    -dim :, :
												]
												__lowercase					     =				in_proj_bias[-dim :]
												# fmt: on





def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				config.decoder_config.hidden_size
				for idx in range(config.decoder_config.decoder_layers				):
								# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
								__lowercase					     =				state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"				)
								__lowercase					     =				state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias"				)
								# next, add query, keys and values (in that order) to the state dict
								__lowercase					     =				in_proj_weight[: hidden_size, :]
								__lowercase					     =				in_proj_bias[:config.hidden_size]
								__lowercase					     =				in_proj_weight[hidden_size : hidden_size * 2, :]
								__lowercase					     =				in_proj_bias[hidden_size : hidden_size * 2]
								__lowercase					     =				in_proj_weight[-hidden_size :, :]
								__lowercase					     =				in_proj_bias[-hidden_size :]
								# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
								__lowercase					     =				state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"				)
								__lowercase					     =				state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias"				)
								# next, add query, keys and values (in that order) to the state dict
								__lowercase					     =				in_proj_weight[: hidden_size, :]
								__lowercase					     =				in_proj_bias[:config.hidden_size]
								__lowercase					     =				in_proj_weight[hidden_size : hidden_size * 2, :]
								__lowercase					     =				in_proj_bias[hidden_size : hidden_size * 2]
								__lowercase					     =				in_proj_weight[-hidden_size :, :]
								__lowercase					     =				in_proj_bias[-hidden_size :]
				# fmt: on





def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				__lowercase					     =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				__lowercase					     =				Image.open(requests.get(lowercase				,     stream=lowercase				).raw				)
				return im





@torch.no_grad()
def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase				,     lowercase = False				):







				"""simple docstring"""



				__lowercase					     =				get_maskformer_config(lowercase				)

				# load original state_dict
				with open(lowercase				,     '''rb'''				) as f:
								__lowercase					     =				pickle.load(lowercase				)
				__lowercase					     =				data['''model''']

				# for name, param in state_dict.items():
				#     print(name, param.shape)

				# rename keys
				__lowercase					     =				create_rename_keys(lowercase				)
				for src, dest in rename_keys:
								rename_key(lowercase				,     lowercase				,     lowercase				)
				read_in_swin_q_k_v(lowercase				,     config.backbone_config				)
				read_in_decoder_q_k_v(lowercase				,     lowercase				)

				# update to torch tensors
				for key, value in state_dict.items():
								__lowercase					     =				torch.from_numpy(lowercase				)

				# load 🤗 model
				__lowercase					     =				MaskFormerForInstanceSegmentation(lowercase				)
				model.eval()

				for name, param in model.named_parameters():
								print(lowercase				,     param.shape				)

				__lowercase     ,	__lowercase					     =				model.load_state_dict(lowercase				,     strict=lowercase				)
				assert missing_keys == [
				    "model.pixel_level_module.encoder.model.layernorm.weight",
				    "model.pixel_level_module.encoder.model.layernorm.bias",
				]
				assert len(lowercase				) == 0, F"Unexpected keys: {unexpected_keys}"

				# verify results
				__lowercase					     =				prepare_img()
				if "vistas" in model_name:
								__lowercase					     =				65
				elif "cityscapes" in model_name:
								__lowercase					     =				65535
				else:
								__lowercase					     =				255
				__lowercase					     =				True if '''ade''' in model_name else False
				__lowercase					     =				MaskFormerImageProcessor(ignore_index=lowercase				,     reduce_labels=lowercase				)

				__lowercase					     =				image_processor(lowercase				,     return_tensors='''pt'''				)

				__lowercase					     =				model(**lowercase				)

				print('''Logits:'''				,     outputs.class_queries_logits[0, :3, :3]				)

				if model_name == "maskformer-swin-tiny-ade":
								__lowercase					     =				torch.tensor(
								    [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]				)
				assert torch.allclose(outputs.class_queries_logits[0, :3, :3]				,     lowercase				,     atol=1E-4				)
				print('''Looks ok!'''				)

				if pytorch_dump_folder_path is not None:
								print(F"Saving model and image processor to {pytorch_dump_folder_path}"				)
								Path(lowercase				).mkdir(exist_ok=lowercase				)
								model.save_pretrained(lowercase				)
								image_processor.save_pretrained(lowercase				)

				if push_to_hub:
								print('''Pushing model and image processor to the hub...'''				)
								model.push_to_hub(F"nielsr/{model_name}"				)
								image_processor.push_to_hub(F"nielsr/{model_name}"				)


if __name__ == "__main__":
		__a    :	Union[str, Any]         =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--model_name""",
		    default="""maskformer-swin-tiny-ade""",
		    type=str,
		    help=("""Name of the MaskFormer model you'd like to convert""",),
		)
		parser.add_argument(
		    """--checkpoint_path""",
		    default="""/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl""",
		    type=str,
		    help="""Path to the original state dict (.pth file).""",
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
		)
		parser.add_argument(
		    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
		)

		__a    :	str         =    parser.parse_args()
		convert_maskformer_checkpoint(
		    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)